Research Product Manager – AI Systems

Granica•San Francisco, CA

24d•$160,000 - $240,000•Onsite

About The Position

Granica is hiring a Research Product Manager to turn frontier AI research into systems that create real value from enterprise data. You’ll work at the intersection of AI/ML systems, structured data, research, and product, helping define how models learn from real-world data, how model quality and emerging capabilities are evaluated, how research becomes production systems, and how technical improvements translate into economic value. Experience with structured or tabular data is a major advantage, but we are equally interested in exceptional product leaders from AI systems, ML infrastructure, evaluation, training/post-training, and applied ML. This is not a traditional feature PM role. You’ll work directly with researchers and engineers to turn technically ambitious ideas into products and systems.

Requirements

5+ years of product leadership or equivalent technical ownership in AI/ML, data systems, infrastructure, or applied research
Strong technical judgment and ability to work directly with researchers and engineers
Experience taking complex technical products or systems from concept to production
Ability to reason about quality, performance, cost, and real-world outcomes
Experience in one or more of: AI / ML platforms or infrastructure, model evaluation, training, post-training, inference, or experimentation, structured / tabular ML, databases, warehouses, lakehouses, or large-scale data platforms, applied ML systems such as recommendation, forecasting, risk, fraud, or ranking

Nice To Haves

Experience with structured, relational, or tabular data
Experience translating research into production systems
Background in engineering, ML, data science, or research
Experience connecting technical improvements to customer value
Comfort operating in a research-driven, highly ambiguous 0→1 environment
AI / ML infrastructure at OpenAI, Google DeepMind, Meta, Anthropic, AWS, or similar
Data infrastructure at Snowflake, Databricks, Microsoft, Google Cloud, or similar
Model evaluation, experimentation, or model-quality systems
Structured-data ML, recommendation, forecasting, risk, fraud, or decision systems
Research engineering or applied science with meaningful product ownership

Responsibilities

Define product direction for AI systems that learn from structured and relational data
Partner with researchers to translate new model capabilities into production systems
Define how model quality and emerging capabilities are evaluated
Identify enterprise ML problems that can move from task-specific models toward shared intelligence
Connect AI systems with enterprise data platforms, warehouses, and lakehouses
Translate model improvements into measurable customer and economic value
Drive research from experiment → system → product → customer value
Shape the roadmap around the highest-value enterprise problems